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MAINT: Refactor graphviz figures to be lecture_specific figures.ipynb assets (#424)
* MAINT: refactor graphviz assets to lecture locations * update markov_chains_I * update markov_chain_II * update networks * clean up * manage merge conflict and ensure black edges
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‎lectures/_static/lecture_specific/graphviz/graphviz_generation.ipynb

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‎lectures/_static/lecture_specific/lake_model/figures.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3c1ab515-e765-48f5-bfaf-35d4f95581fd",
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"metadata": {},
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"source": [
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"# Figures for Lake Model Lecture"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "45727b3e-f531-4a31-b838-840ccf9d3c02",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: graphviz in /Users/mmcky/anaconda3/envs/quantecon/lib/python3.11/site-packages (0.20.3)\n"
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]
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}
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],
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"source": [
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"!pip install graphviz"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "68e95ec8-877e-416a-a8ff-665715dfe222",
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"metadata": {},
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"outputs": [],
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"source": [
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"from graphviz import Digraph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "92a57207-4d79-4d7b-98c2-aea1a1e35f57",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'lake_model_worker.png'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create Digraph object\n",
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"G = Digraph(format='png')\n",
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"G.attr(rankdir='LR')\n",
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"\n",
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"# Add nodes\n",
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"G.attr('node', shape='circle')\n",
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"G.node('1', 'New entrants', color='blue')\n",
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"G.node('2', 'Unemployed')\n",
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"G.node('3', 'Employed')\n",
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"\n",
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"# Add edges\n",
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"G.edge('1', '2', label='b')\n",
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"G.edge('2', '3', label='λ(1-d)')\n",
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"G.edge('3', '2', label='α(1-d)')\n",
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"G.edge('2', '2', label='(1-λ)(1-d)')\n",
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"G.edge('3', '3', label='(1-α)(1-d)')\n",
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"\n",
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"# Save Plot\n",
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"G.render(filename='lake_model_worker')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6c2af58-4d38-44c3-adb0-d1a92a344e51",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎lectures/_static/lecture_specific/lake_model/lake_model_worker

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digraph {
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rankdir=LR
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node [shape=circle]
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1 [label="New entrants" color=blue]
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2 [label=Unemployed]
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3 [label=Employed]
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1 -> 2 [label=b]
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2 -> 3 [label="λ(1-d)"]
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3 -> 2 [label="α(1-d)"]
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2 -> 2 [label="(1-λ)(1-d)"]
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3 -> 3 [label="(1-α)(1-d)"]
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}

‎lectures/_static/lecture_specific/lake_model/lake_model_worker.png

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‎lectures/_static/lecture_specific/markov_chains_I/Hamilton

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digraph {
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rankdir=LR
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ng
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mr
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sr
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ng -> ng [label=0.971]
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ng -> mr [label=0.029]
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mr -> ng [label=0.145]
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mr -> mr [label=0.778]
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mr -> sr [label=0.077]
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sr -> mr [label=0.508]
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sr -> sr [label=0.492]
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}

‎lectures/_static/lecture_specific/markov_chains_I/Hamilton.png

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‎lectures/_static/lecture_specific/markov_chains_I/Temple

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digraph {
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rankdir=LR
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Growth
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Stagnation
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Collapse
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Growth -> Growth [label=0.68]
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Growth -> Stagnation [label=0.12]
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Growth -> Collapse [label=0.20]
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Stagnation -> Stagnation [label=0.24]
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Stagnation -> Growth [label=0.50]
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Stagnation -> Collapse [label=0.26]
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Collapse -> Collapse [label=0.46]
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Collapse -> Stagnation [label=0.18]
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Collapse -> Growth [label=0.36]
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}

‎lectures/_static/lecture_specific/markov_chains_I/Temple.png

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‎lectures/_static/lecture_specific/markov_chains_I/figures.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "43b11347-069e-4c50-b093-4635361408fc",
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"metadata": {},
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"source": [
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"# Figures for Markov Chains I"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "38b0e8a2-acab-4b27-95b3-96174c7d6863",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: graphviz in /Users/mmcky/anaconda3/envs/quantecon/lib/python3.11/site-packages (0.20.3)\n"
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]
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}
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],
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"source": [
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"!pip install graphviz"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "606da6ac-e8b7-49c6-9d88-142e18e02bba",
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"metadata": {},
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"outputs": [],
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"source": [
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"from graphviz import Digraph"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87430938-745b-45d3-9895-937bc357432d",
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"metadata": {},
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"source": [
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"## Example 1\n",
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"\n",
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"Hamilton on US unemployment data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "eff7a6b5-98d4-4f26-b72f-7100a911644e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hamilton.png'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dot = Digraph(format='png')\n",
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"dot.attr(rankdir='LR')\n",
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"dot.node(\"ng\")\n",
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"dot.node(\"mr\")\n",
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"dot.node(\"sr\")\n",
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"\n",
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"dot.edge(\"ng\", \"ng\", label=\"0.971\")\n",
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"dot.edge(\"ng\", \"mr\", label=\"0.029\")\n",
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"dot.edge(\"mr\", \"ng\", label=\"0.145\")\n",
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"\n",
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"dot.edge(\"mr\", \"mr\", label=\"0.778\")\n",
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"dot.edge(\"mr\", \"sr\", label=\"0.077\")\n",
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"dot.edge(\"sr\", \"mr\", label=\"0.508\")\n",
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"\n",
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"dot.edge(\"sr\", \"sr\", label=\"0.492\")\n",
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"dot\n",
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"\n",
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"dot.render(filename='Hamilton')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7effdaa6-ee97-4634-811c-1b90d7e95543",
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"metadata": {},
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"source": [
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"## Exercise 1\n",
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"\n",
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"Solution 2:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bb57212a-c0f2-4922-a549-0edea60ec590",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Temple.png'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dot = Digraph(format='png')\n",
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"dot.attr(rankdir='LR')\n",
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"dot.node(\"Growth\")\n",
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"dot.node(\"Stagnation\")\n",
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"dot.node(\"Collapse\")\n",
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"\n",
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"dot.edge(\"Growth\", \"Growth\", label=\"0.68\")\n",
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"dot.edge(\"Growth\", \"Stagnation\", label=\"0.12\")\n",
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"dot.edge(\"Growth\", \"Collapse\", label=\"0.20\")\n",
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"\n",
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"dot.edge(\"Stagnation\", \"Stagnation\", label=\"0.24\")\n",
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"dot.edge(\"Stagnation\", \"Growth\", label=\"0.50\")\n",
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"dot.edge(\"Stagnation\", \"Collapse\", label=\"0.26\")\n",
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"\n",
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"dot.edge(\"Collapse\", \"Collapse\", label=\"0.46\")\n",
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"dot.edge(\"Collapse\", \"Stagnation\", label=\"0.18\")\n",
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"dot.edge(\"Collapse\", \"Growth\", label=\"0.36\")\n",
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"\n",
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"dot\n",
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"\n",
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"dot.render(filename='Temple')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7aca758c-e819-4d01-a2aa-c5fd36f0e2ad",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎lectures/_static/lecture_specific/markov_chains_II/Irre_1

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digraph {
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rankdir=LR
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poor
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"middle class"
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rich
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poor -> poor [label=0.9]
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poor -> "middle class" [label=0.1]
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"middle class" -> poor [label=0.4]
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"middle class" -> "middle class" [label=0.4]
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"middle class" -> rich [label=0.2]
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rich -> poor [label=0.1]
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rich -> "middle class" [label=0.1]
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rich -> rich [label=0.8]
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}

‎lectures/_static/lecture_specific/markov_chains_II/Irre_1.png

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‎lectures/_static/lecture_specific/markov_chains_II/Irre_2

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digraph {
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rankdir=LR
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poor
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"middle class"
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rich
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poor -> poor [label=1.0]
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"middle class" -> poor [label=0.1]
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"middle class" -> "middle class" [label=0.8]
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"middle class" -> rich [label=0.1]
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rich -> "middle class" [label=0.2]
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rich -> rich [label=0.8]
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}

‎lectures/_static/lecture_specific/markov_chains_II/Irre_2.png

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‎lectures/_static/lecture_specific/markov_chains_II/example4

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digraph {
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rankdir=LR
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0
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1
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0 -> 1 [label=1.0]
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1 -> 0 [label=1.0]
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}

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